video of working prototype: https://youtu.be/1ie1ujZIjB8
1 Raspberry PI and connecting with Arduino 2
1.1 Installing Operating System 2
1.2 Identifying usb connected devices 2
1.3 Face Tracking opencv tutorial 2
1.4 Arduino and Raspberry - Hardware setup – Voltage problems 3
1.5 Raspberry Pi - Motion – package that turns your RPI into smart CCTV. 4
1.6 Raspberry Pi - Face Recognition 4
2 SPEECH recognition on raspberry Pi 6
2.1 Problems with pseye 4 microphone array 6
3 4 april 2020 – breakthrough – and change of direction ? 12
3.1 installed picottss by downloading the Debian wheels into raspian 16
3.2 Adding Interactivity – simple learning algorithm 16
4.1 MozillaDeepSpeech Python – lets try realtime 21
6 Optimising camera behaviour: 25
7 Trust your guts (moustache problem) 26
A lot of python tutorials, especially video ones neglect /omit installation process, importing packages. Because of vast landscape of platforms and builds, you have to most often do a lot of research prior to accomplishing the first test run without errors.
29 march 2020
I installed Raspbian on a Raspberry Pi 3B+ Rev 1.3
Please refer to :
https://www.youtube.com/watch?v=Zhd-i6DKlAU
or official guides
https://www.raspberrypi.org/help/noobs-setup/2/
Traditionally after connecting it to a TV with keyboard and mouse , and finishing installation , I entered configuration , enabling ssh - https://pimylifeup.com/raspberry-pi-ssh/ and interfacing options, as well as booting to command line. I followed with installing Arduino IDE on my Windows 10 Laptop. And building a circuit using TinkerCad
Proceeded with searching around Text to speech Synthesis engines
Found this blog entry, massively outdated Still Dan Fountain is the guy behind Google Text to speech shell I use in the end https://web.archive.org/web/20151016182428/http://danfountain.com/2013/03/raspberry-pi-text-to-speech/
Learned how to display connected devices(?) using dev/tty*to identify plugged in devices, this helps to identify
How to address Arduino Serial port using python
Unplug you Arduino , issue ls dev/tty* plug it in and enter ls dev/tty* again to see the port of Arduino.
It will be something in the lines of “dev/ttyUSB0” and depends on other usb devices connected to your raspberry pi.
Using pyserial
Sudo pip3 install pyserial
Miller, L. (2019) Face Tracking OpenCV, Python, & Arduino. Learn Robotics. 23 May. Av: https://www.learnrobotics.org/blog/face-tracking-opencv/amp/ (Acc:
Large portion of my code was inspired by this post/tutorial by Miller (2019). Unfortunately her code is neither very well commented nor adapted to extending to any other situation. In Addition you have to adapt the python 2 syntax to work with python 3 libraries.
It is worth remarking that solution proposed is not safe for your Arduino ( never connect more than one servo to single Arduino board as power source). If your system will start generating erratic notifications about plugged in / out USB device it means that there is not enough power for Arduino due to the burden created by servos. Arduino cannot supply enough current for servos.
Connecting multiple servos to one source in parallel we allow identical voltage drop across all servos, but current varies , total current
Voltage drop on the diagram is 6 Volts, however servos drained batterries quickly. Normal batteries are good for equipment that draws mA not amps. 5 servos needing 6-7.2 V may put a load on battery.
I repurposed one of many charging usb cables , to replace battery pack with mobile power bank battery (pic + specs). Till now it seems the most efficient power source that does not overload servos and provides enough current to move them all simultaneously.
Figure . Tinkercad electronic circuit diagram.
Motion is a configurable command-line software turn your raspberry pi into CCTV hub with http streaming capabilities. There is remote control interface based on the HTTP request, to manipulate servos. Although junky, can be utilised if other web interface would be used to integrate all functionalities into one page application.
Followed this guidance https://www.raspberrypi.org/forums/viewtopic.php?t=7397
However you need to take into account the post is old as well and some modifications are required
webcam_localhost off
Is now
stream_localhost off
and
control_localhost off
is
webcontrol_localhost off
The program allows multiple cameras and separate configurations for each . There is a lot of additional configuration – including servo controls, this is an option if you do not plan complex animations, and probably just mounted your cameras on servo rigs.
Robopathshala (2018) Face Detection+recognition. No date*. Instructable Circuits*. Av: https://www.instructables.com/id/Face-Detectionrecognition/ (Acc:
I followed this simple tutorial by Robopathshala(2018) for learning on example how to generate recognition model for faces
Obviously, again certain parts are outdated – it is needed to transform them for compatibility with opencv version 4
Outdated lines:
recognizer = cv2.createLBPHFaceRecognizer();
# . . .
font=cv2.cv.InitFont(cv2.cv.CV_FONT_HERSHEY_COMPLEX_SMALL,5,1,0,4)
# . . .
rec.load("F:/Program Files/projects/face_rec/faceREC/trainingdata.yml")
was changed to:
recognizer = cv2.face.LBPHFaceRecognizer_create()
# . . .
font = cv2.FONT_HERSHEY_SIMPLEX
# . . .
rec.read(
load method of the recognizer has been renamed to read in recent versions of opencv. Please refer to OpenCV documentation https://opencv.org/ and try to google search errors thrown by python interpreter.
and additionally I adapted the sourcecode to work with my streaming server.
Installing SpeechRecognition requires PyAudio. PyAudio is a dependency of SpeechRecognition.
Since there is no PyAudio release with support for Windows AMD64 and python 37 combination I had to browse to https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyaudio to download unofficial wheel file for pip
I Was afraid that I will need to compile PortAudio C Library, however it seems that it is included in wheel file, as test script started working immediately.
Raspberry pi
Arecord hangs and no sound is recorded
Found this blogpost : https://www.cnx-software.com/2019/08/30/using-sony-ps3-eye-camera-as-an-inexpensive-microphone-array/
Didn’t found /etc/udev/rules.d/70-alsa-permanent.rules
But found /etc/udev/rules.d/99-com.rules
Added files as those that should exist(different Raspbian?) per tutorial
Seems like video settings
I2c gpio rpivid , maybe camera modules?
This is output of alsa identified devices:
pi@raspberrypi:~/duncanStream $ arecord -l
**** List of CAPTURE Hardware Devices ****
card 1: CameraB409241 [USB Camera-B4.09.24.1], device 0: USB Audio [USB Audio]
Subdevices: 1/1
Installed pulseaudio
Sometimes I think is it not worth to spend so much time trying to set up one cheap camera PsEye as microphone, since I could buy dedicated hardware. There is some sense in Adafruit creating dedicated hardware it helps to avoid this cumbersome process. I feel like I will have to learn whole linux before setting this to work.
After initial struggle I calmed down , found the https://www.raspberrypi.org/forums/viewtopic.php?t=186739 and author post claims his configuration works.
# ps3 eye
## Suggested by http://julius.sourceforge.jp/forum/viewtopic.php?f=9\&t=66
pcm.array {
type hw
card CameraB409241
}
pcm.array_gain {
type softvol
slave {
pcm "array"
}
control {
name "Mic Gain"
count 2
}
min_dB -10.0
max_dB 5.0
}
pcm.cap {
type plug
slave {
pcm "array_gain"
channels 4
}
route_policy sum
}
pcm.!default {
type asym
playback.pcm {
type plug
slave.pcm {
@func getenv
vars [ ALSAPCM ]
default "hw:PCH"
}
}
capture.pcm {
type plug
slave.pcm "cap"
}
}
pcm.!default {
type hw
card 2
}
ctl.!default {
type hw
card 2
}
This in turn helped me to understand how asoundrc works https://www.alsa-project.org/wiki/Asoundrc
https://www.raspberrypi.org/forums/viewtopic.php?t=186739 helpful somewhat
above link considers multiple capture devices but
pcm.array
{
type hw
card CameraB409241
format S16_LE
rate 16000
}
pcm.internal
{
type hw
card 0
}
pcm.array_gain {
type softvol
slave {
pcm "array"
}
control {
name "Mic Gain"
count 2
}
min_dB -10.0
max_dB 5.0
}
pcm.cap {
type plug
slave {
pcm "array_gain"
channels 4
}
route_policy sum
}
pcm.!default
{
type asym
playback.pcm
{
type plug
slave.pcm "usb"
}
capture.pcm
{
type plug
slave.pcm "cap"
}
}
ctl.!default
{
type asym
playback.pcm
{
type plug
slave.pcm "usb"
}
capture.pcm
{
type plug
slave.pcm "cap"
}
}
Not sure but it appears that ALSA has problem anyway
Pip install SpeechRecognition
I tried to use speech_recognition module with microphone but pyaudio has problem interfacing with alsa devices, even if they are working now on their own (from terminal sudo arecord -D plughw:1 -d 5 -c 4 -r 48000 -f S16_LE t3pore.wav )
So I gave up finally and will attempt to run recording using subprocess.popen from python and opening the file using speech_recognition.WavFile().
This may reduce capabilities, constricting the time of the recording but may work.
Finally I discovered that speech_recognition , or google tts API , doesn’t handle 4 channels in current set-up. Therefore I recorded an audio file with
sudo arecord -D plughw:1 -d 5 -c 2 -r 48000 -f S16_LE v.wav
and modified listen.py program to accept file name as a parameter.
Next step is to incorporate logic from listen.py into my main duncanStream file, so each time after seeing face he will start recording.
Prior to following I had to change ownership of the folder , because I created it as a root and each time I tried to create a file I had to sudo and python scripts failed due to lack of permissions.
sudo chown -R $USER /duncanFolder
-R for recursive $USER for current user (can be pi in my case)
import subprocess
persons = ["Human", "Bolek", "Maciek", "Ania"]
greetings =
["hello.mp3", "bolek.mp3", "maciek.mp3", "ania.mp3"]
LINUX = True
def hello(id):
global greetings
print("hello" + persons[id])
seconds = str(5)
tempfilename = "tmp.wav"
if LINUX:
# subprocess.run(
# ["play", greetings[id], "pitch", "500"])
subprocess.run(
["play", greetings[id], "pitch", "500"])
subprocess.run(["arecord", "-D", "plughw:1", "-d", seconds,
"-c", "2", "-r", "48000", "-f", "S16_LE", tempfilename])
# sudo arecord -D plughw:1 -d 5 -c 2 -r 48000 -f S16_LE v.wav
hello(1)
this script successfully runs , creates a file after greeting interlocutor
Now time for quick dirty test : greet, record tmp.wav file and open it with subprocess running listen.py script with name of the file as argument.
TEST.PY:
import subprocess
persons = ["Human", "Bolek", "Maciek", "Ania"]
greetings = ["hello.mp3", "bolek.mp3", "maciek.mp3", "ania.mp3"]
LINUX = True
def hear():
print("listening...")
seconds = str(5)
tempfilename = "tmp.wav"
if not LINUX:
input("write something")
subprocess.run(["arecord", "-D", "plughw:1", "-d", seconds,
"-c", "2", "-r", "48000", "-f", "S16_LE", tempfilename])
print("recognizing...")
subprocess.run(["python3", "listen.py", "-f", tempfilename])
def hello(id):
global greetings
print("hello" + persons[id])
seconds = str(5)
tempfilename = "tmp.wav"
if LINUX:
# subprocess.run(
# ["play", greetings[id], "pitch", "500"])
subprocess.run(
["play", greetings[id], "vol", "0.25", "pitch", "500"])
# sudo arecord -D plughw:1 -d 5 -c 2 -r 48000 -f S16_LE v.wav
hear()
hello(1)
LISTEN.PY :
# Requires PyAudio and PySpeech.
import speech_recognition as sr
import argparse
if __name__ == '__main__':
# construct the argument parser and parse command line arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--file", type=str, required=True,
help="file to recognize ")
args = vars(ap.parse_args())
print(args)
# Record Audio
r = sr.Recognizer()
with sr.WavFile(args['file']) as source:
print("Say something!")
audio = r.record(source)
# Speech recognition using Google Speech Recognition
try:
# for testing purposes, we're just using the default API key
# to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
# instead of `r.recognize_google(audio)`
print("You said: " + r.recognize_google(audio))
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
print(
"Could not request results from Google Speech Recognition service; {0}".format(e))
pi@raspberrypi:~/duncanStream $ python3 test.py
helloBolek
bolek.mp3:
File Size: 5.86k Bit Rate: 32.0k
Encoding: MPEG audio
Channels: 1 @ 16-bit
Samplerate: 24000Hz
Replaygain: off
Duration: 00:00:01.46
In:98.4% 00:00:01.44 [00:00:00.02] Out:34.6k [ | ] Clip:0
Done.
listening...
Recording WAVE 'tmp.wav' : Signed 16 bit Little Endian, Rate 48000 Hz, Stereo
recognizing...
{'file': 'tmp.wav'}
Say something!
You said: what is your name
Hurray. Now it’s time to incorporate the logic into main script, so no separate calls are done. I need to start thinking about writing some classes and providing modularity to the whole system.
https://bugs.launchpad.net/raspbian/+bug/1835974
research over problems with using pico2wav , that seems removed from Raspbian, because it is not free led me to:
https://bugs.launchpad.net/raspbian/+bug/1835974
wget -q https://ftp-master.debian.org/keys/release-10.asc -O- |
apt-key add -
echo "deb http://deb.debian.org/debian buster non-free" >>
/etc/apt/sources.list
apt-get update
apt-get install libttspico0
or without adding the repo:
wget http://ftp.us.debian.org/debian/pool/non-free/s/svox/libttspico0_1.0+git20130326-9_armhf.deb
wget http://ftp.us.debian.org/debian/pool/non-free/s/svox/libttspico-utils_1.0+git20130326-9_armhf.deb
apt-get install -f ./libttspico0_1.0+git20130326-9_armhf.deb
./libttspico-utils_1.0+git20130326-9_armhf.deb
worked and now I have nice sounding fallback if googtts will start playing up
additionally I forked snipssuperTTS to cache already produced sounds, however do not intend going this route yet. The less dependencies on the network , the better for now. https://gist.github.com/Osmiogrzesznik/4636fa4ae59d26079c47604561509565
https://github.com/howardabrams/cookie-python/blob/master/Animal%20Guessing%20Game.ipynb
above is good explanation of how the algorithm works
I found animal guessing game
https://github.com/ninovolador/animal-guesser
that fits well as old classy ai for kids and some addition to machine learning
guesser.py
I forked and modified the code so it will use polish language as well, and to have some base to start from
I reached to the point where I have to make computer recognize my voice. But’ should I rely on Google STT from Speech_Recognition?
There is issue : if set to pl-PL it does not produce translated output.
Additionally there will be a need for AI to ask for confirmation – did it understood correctly the spoken word.
To test things on windows I will need to install :
pip install pyttsx3
I am interested in Mozilla DeepSpeech project and feel like developing it in parallel. It may yield better results, and will not require network, since it is local . Current Speech_recognition uses google API key reverse-engneered from chrome so it is prone to fail in the future if Google will revoke it.
First let’s read more : https://hacks.mozilla.org/2019/12/deepspeech-0-6-mozillas-speech-to-text-engine/
API Documentation : https://deepspeech.readthedocs.io/en/v0.6.1/
Raspberry Pi4 : https://www.seeedstudio.com/blog/2020/01/23/offline-speech-recognition-on-raspberry-pi-4-with-respeaker/comment-page-1/
Elaine Wu (2020) claims :
Installation
Raspberry Pi 4/3B
The pre-built wheel package for arm7 architecture is set to use.tflite model by default and installing it as easy as just
pip3 install deepspeech
Let’s test it out!
I have run the pip installation command on windows 10 first to keep similar environments for testing
Obviously :
Figure . windows . need to use --user. Windows oh windows !
Figure . Interesting - especially these warnings. May result in some problems later.
It seems that default models are heavy weight. Elain Wu(2020) warns about the size of the file : 4 GB is a little tough for RPI isn’t it? We can download the prewview wheel with tflite support
Ooops link on the website doesn’t work,
Let’s go to the source then . Source – code. I mean Github :
https://github.com/mozilla/DeepSpeech/releases
There we can see history of latest releases of the package . And my oh
my , does the mozilla approaches the things really well. Builds are done
on all platforms. Just have a look :
Figure . Github deepspeech release builds
So every python version and processor architecture , and os combination seems to be present here. OpenSource, baby ! (done by people knowing what they are doing and doing it since looong time)
I downloaded the py37 win amd64 version (whl file for pip)
pip install --user /PROJECT/deepspeech-0.7.0a3-cp37-cp37m-win_amd64.whl
I found identical problem on discourse – unsolved , and proceeded with adding my comment
pi@raspberrypi:~/animal-guesser/deepspeech-0.6.1-models $ deepspeech --model output_graph.pbmm --audio tds.wav
Loading model from file output_graph.pbmm
TensorFlow: v1.14.0-21-ge77504a
DeepSpeech: v0.6.1-0-g3df20fe
ERROR: Model provided has model identifier '='+;', should be 'TFL3'
Error at reading model file output_graph.pbmm
Traceback (most recent call last):
File "/home/pi/.local/bin/deepspeech", line 10, in <module>
sys.exit(main())
File "/home/pi/.local/lib/python3.7/site-packages/deepspeech/client.py", line 113, in main
ds = Model(args.model, args.beam_width)
File "/home/pi/.local/lib/python3.7/site-packages/deepspeech/__init__.py", line 42, in __init__
raise RuntimeError("CreateModel failed with error code {}".format(status))
RuntimeError: CreateModel failed with error code 12288
By googling “ERROR: Model provided has model identifier '='+;', should be 'TFL3' RuntimeError: CreateModel failed with error code 12288
I found this issue:
one of the answers Lissyx recommends switching to tflite model, due to the fact that binaries are pre-configured to use TensorFlow Lite runtime. Since the error
As documented, RPi3/4 binaries are using the TensorFlow Lite runtime, so you need to pass output_graph.tflite and not output_graph.pbmm.
deepspeech --model output_graph.tflite --audio myaudio.wav
Loading model from file output_graph.tflite
TensorFlow: v1.14.0-21-ge77504a
DeepSpeech: v0.6.1-0-g3df20fe
INFO: Initialized TensorFlow Lite runtime.
Loaded model in 0.0428s.
Warning: original sample rate (48000) is different than 16000hz. Resampling might produce erratic speech recognition.
Running inference.
one two one two on to three four five six
Inference took 15.384s for 15.000s audio file.
Weird thing though , using files recorded with 16khz sample rate gives gibberish output, but contraversely , when warning is displayed while infering 48000 recording - it gives normal results.
Time to infer is longer when sample rate is as recommended.
Although I am more comfortable with Javascript I choose to follow Python, since there is much more resources in connection to Machine Learning.
No realtime – at least no tutorials, and documentation seems to have hgigh threshold for python skills
We will go with rec and deepspeech
rec is part of sox (sudo apt install sox)
$ rec --channels 1 --rate 16k tmp2.wav silence 1 0.01 2% 1 3.0 2%
Mono 16000 sample rate saved to tmp2.wav file with silence effect – truncates silence (behaves as automatic speech detection, saving only needed portion of file)
Then we will infer the content with:
deepspeech --model output_graph.tflite --lm lm.binary --trie trie --audio tmp2.wav
effect is disappointing , the script needs the time to record, then inference may take up to twice the length of the recording.
https://www.youtube.com/watch?v=BvAos--wY8s
I found however some resource:
https://discourse.mozilla.org/t/real-time-deepspeech-analysis-using-built-in-microphone/42669
in down below the discussion ( apart from offtopic on difference between shell and python) I found extended version
next day 6 april 2020 I just wanted to find it be name of the post
https://github.com/mozilla/DeepSpeech/tree/master/examples/
deepspeech undergoes so much changes that sometimes it is futile to use links to point others to content by links.
Examples – this is what we: other people struggling , and I lacked . By moving examples from repo itself and into separate repository, they instantly broke typical procedure of installing a package, or repo. It is in the reademe where you find the links to everywhere, or examples, and they did not provided any clue in the README to the new location of examples. This is why so many people struggle to find their first steps into MDS.
https://github.com/mozilla/DeepSpeech-examples/tree/r0.6/mic_vad_streaming
And this comes back to the previous problem – pyAudio needs to be somehow configured to work with pseye
Microphones array.
For now Lets put the Duncan together
Add subprocess call for animal guessing game
Remember about absolute paths, since processes are executed in the origin directory.
https://www.tinkercad.com/things/gqR3O1cl44S-duncan-servo-setup/editel
TODOS:
Niewyraznie gada
Removing the serial output by creating debug function
It stopped working every time when serial debug output was turned off
The main culprit was lack of at least one millisecond delay during loop execution on arduino. I don’t know why yet but seems rather like some C++ optimisation thing or serial synchronisation thing. Maybe the case is characters are written to serial by computer slower and Arduino must wait a little bit after Serial.available()
Faces are always min d=65
Face will never be detected on the edge of screen, just before. Need to take this into account
And 400-65 maximum = 335
Try to implement ROTATE IF DISSAPEARED algorithm
Arduino may treat 65 and 335 as zero and maximum respectively
You can calculate it on Arduino
Using Jinja2 (in python flask by default) with vue.js creates problem of clashing “{{}}” symbols
Trust your guts – I though about this problem just at the start , but handwaved , and somehow few minutes into coding , completely forgot about it and waste few hours on debugging perfectly working vue.js code
Following test user advice , it would be more interesting for now for casual audience to see robot moving steered by their own decisions. Remot control is simpler, but provides more immediate value. As expected when I finished users are more interested in steering robot than to interact with it .
Used sockets
Zdalne sterowanie duncanem zrobiloby wiecej wrazenia
T: powiedz slowo